Probabilistic reversal learning deficits in patients with methamphetamine use disorder - a longitudinal pilot study
Abstract: Methamphetamine use disorder (MUD) is increasing worldwide and commonly associated with learning deficits. Little is known the about underlying trajectories, i.e., how the affected higher-order cognitive functions develop over time and with respect to abstinence and relapse. A probabilistic reversal learning (PRL) paradigm was implemented to uncover the microstructure of impulsive choice and maladaptive learning strategies in 23 patients with MUD in comparison with 24 controls. Baseline data revealed fewer optimal choices and a pattern of altered learning behavior from negative and positive feedback in patients suggesting impairments in flexibly-adapting behavior to changes of reward contingencies. Integrating longitudinal data from a follow-up assessment after 3 months of specific treatment revealed a group-by-time interaction indicating a normalization of these cognitive impairments in patients with MUD. In summary, our study demonstrates behavioral correlates of maladaptive decision-making processes in patients with MUD, which may recover after 3 months of MUD-specific therapy paving the way for further learning-based interventions. Limited by a small sample size, the results of this pilot study warrant replication in larger populations
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
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Frontiers in psychiatry. - 11 (2020) , 588768, ISSN: 1664-0640
- Ereignis
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Veröffentlichung
- (wo)
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Freiburg
- (wer)
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Universität
- (wann)
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2021
- Urheber
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Pilhatsch, Maximilian
Pooseh, Shakoor
Junke, Alexandra
Kohno, Milky
Petzold, Johannes
Sauer, Cathrin
Smolka, Michael
- DOI
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10.3389/fpsyt.2020.588768
- URN
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urn:nbn:de:bsz:25-freidok-2206171
- Rechteinformation
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Kein Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:25 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Pilhatsch, Maximilian
- Pooseh, Shakoor
- Junke, Alexandra
- Kohno, Milky
- Petzold, Johannes
- Sauer, Cathrin
- Smolka, Michael
- Universität
Entstanden
- 2021
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